Multiple Imputation in Mplus

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• Mplus fully automates the analysis and pooling phases • Analyzing imputed data sets requires a small change to the DATA command, but the remaining commands are identical to an ML analysis • There is no need to list the variances and covariances for incomplete explanatory variables because these variables are now complete
DATA:! file = employeeimplist.dat; type = imputation; ! List of imputation file names;! ! Imputation data;! VARIABLE:! names = jobperf tenure wbeing jobsat turnover iq;! usevariables = jobperf tenure wbeing jobsat turnover;! missing = all (-99);! centering = grandmean(tenure wbeing jobsat);! ANALYSIS:! estimator = ml;! MODEL:! jobperf on wbeing (b1);! jobperf on jobsat (b2);! jobperf on tenure (b3);! jobperf on turnover (b4);! MODEL TEST:! b1 = 0; b2 = 0; b3 = 0; b4 = 0;! OUTPUT:! standardized sampstat patterns;! ! ! ! !
Page 2 and 3: • Data set containing scores from
Page 4 and 5: • TITLE (optional) • DATA (same
Page 6 and 7: • All variables listed on the USE
Page 8 and 9: • Specifies characteristics of th
Page 10 and 11: DATA:! file = employee.dat;! VARIAB
Page 12 and 13: • Mplus saves each imputed data s
Page 16 and 17: SAMPLE STATISTICS! ! NOTE: These ar
Page 18 and 19: • The Wald statistic (a chi-squar
Page 20 and 21: • Because the continuous are cent
Page 22 and 23: ! R-SQUARE! ! ! Observed Two-Tailed
Page 24 and 25: • After creating the complete dat
Page 26: • Tabular presentations of missin

Multiple Imputation in Mplus

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